A Regression analysis has an objective that goes beyond just checking if two variables are related. Regression is a technique that allows to quantify and infer the influence or not of one or more independent variables (predictors) on a dependent variable (outcome). 

This statistical technique also allows, through an equation, to predict unknown values of the dependent variable through the already known values of the predictor variables.

To illustrate this type of analysis, imagine that a researcher wants to research the possible factors that can interfere with the rates of "bad" cholesterol (LDL) in 300 patients of a research. In this work he evaluated some possible predictors, such as: sex, age, frequency   of physical activity, abdominal diameter, weight, height, use or not of corticosteroids and presence or not of an unbalanced diet. Regression Analysis can tell which of these predictors actually significantly impact the outcome (LDL rate), in addition to quantifying this impact. With this, it is possible to create a regressive model, through which, from the values collected from the independent variables, the expected value for the dependent variable can be predicted.